Railway infrastructure is expensive both in terms of rolling stock and track. Although generally regarded as one of the safest forms of transport, railway accidents are common and frequently fatal. Of the most dangerous of such accidents are collisions between trains or between trains and vehicles crossing the track in the path of an oncoming train; and derailments consequent to foreign objects placed either willfully or accidentally on the line. Such objects may or may not be seen by the engine driver prior to collision therewith, especially at night. Under these circumstances, the best that can usually be achieved is to reduce the collision speed. As statistics of rail accidents demonstrate only too well, mere reduction of collision speed might significantly reduce the damage, even if the train is not able to get to a complete standstill. Bearing in mind the trend to increase the speed of rolling stock with the consequent increase in stopping distance, the drawbacks of existing approaches and the rising costs of insurance claims and premiums are likely to become even more severe.
The prior art disclose various approaches to preventing or signalling potential collisions between rolling railstock. For example, in U.S. Pat. No. 3,365,572 (Strauss) a modulated laser beam is directed from opposite ends of railstock so that the corresponding laser beams transmitted from two approaching trains may be detected by the other train, allowing remedial action to be taken. Likewise, image processing techniques are known both for vehicle recognition as in U.S. Pat. No. 5,487,116 (Nakano et al.) and for detecting a vehicle path along which a vehicle is travelling as in U.S. Pat. No. 5,301,115 (Nouso). Further, the use of Global Positioning Systems (GPS) on railstock has been proposed in U.S. Pat. No. 5,574,469 (Hsu) for improving the collision avoidance between two locomotives.
Existing systems are known which exploit the flow of current through one rail and its return through the other rail in order to detect an electrically conductive object placed on the track thereby shorting the rails. However, such systems are practical only for electrical railway systems having two tracks for providing live and return paths for the electric current. Specifically, they are not suitable for railway systems employing overhead power lines; nor for those systems which employ a third rail either mid-way between the regular rail or alongside one of the rails. Moreover, they are unsuitable for detecting non-conductive obstacles on the track. Yet a further drawback of such known systems is that they are static.
Also known is an obstacle detection system for monitoring a railroad track far ahead of a train so as to warn against stationary or moving obstacles. The system comprises a transceiver mounted on the train and a number of relays deployed along the railroad track. The moving train emits a laser beam which is picked up by one of the relays along the track and coupled into a fiberoptic cable which thus relays the laser signal along a long distance of track ahead of the train. The fiberoptic cable is coupled to an exit port for directing the laser beam towards a retroreflector disposed diagonally across the tracks such that an obstacle placed on the track ahead of the moving train obstructs the laser beam. The retroreflected laser beam retraces its path along the fiberoptic cable back to the train allowing an on-board processor to determine the presence of the obstacle in sufficient time to enable corrective action to be taken. Such a system enables detection of an obstacle which is far ahead of the train and out of direct sight thereof However, it requires expensive infrastructure and maintenance.
Systems are also known containing a database wherein there is stored data representative of a complete length of track. During operation, each imaged section is compared with the corresponding section of track in the database in order to infer therefrom whether the track image corresponds to the database or not; the inference being that any mismatch is due to an obstacle on the imaged section of the track.
Such an approach is hardly feasible for mass transit systems based on perhaps hundreds of kilometers of track (if not more). It is clear that to store a database of a complete image of a track stretching across a route of many hundreds of kilometers would require a memory capacity rendering such an approach hardly practicable. Thus, such approaches have, in the past, been confined to relatively short lengths of track such as may be found, for example, in factories, shipyards and the like.
Such an approach is disclosed for example in JP 59 156089 which requires a large capacity memory in which there is stored a photographed image of the route which is to be traveled by the vehicle. A video comparator compares each instantaneous image of the track with a corresponding image in the storage device so as to interpret any mismatch as an obstacle on the tracks. Such an approach is subject to the various drawbacks highlighted above as well as requiring that the actual location of each imaged section of the tracks be known. Otherwise, it is not possible to compare the database image with the instantaneous image of the track section obtained during motion of the vehicle. This, in turn, requires synchronization between the "rolling" image of the track during motion of the vehicle and the track image stored in the database.
Typically, such synchronization is effected from a knowledge of the speed of the vehicle and elapsed time which can be translated into distance traveled so that from an initial starting point (time=zero) the actual distance traveled by the vehicle can be determined. This, in turn, allows determination as to which stored section of track in the database must be compared with the instantaneous image for the purpose of obstacle detection.
JP 05 116626 discloses an obstacle detection system for use with rolling stock wherein an infrared camera is mounted on an engine in conjunction with an image-processing means for determining whether an obstacle is present on the rails. Here again however, the algorithm is based on the use of a pre-stored database of the complete track such that each imaged frame is compared with the pre-stored database so as to construe any discrepancy as an obstacle.
As noted above, with reference to cited JP 59 156089, this requires a very high volume memory which renders such a system virtually impractical for mass-transit systems covering large distances; and further requires synchronization.
One of the problems associated with obstacle detection systems for track-led vehicles is the fact that it is obviously necessary to provide advanced warning of an obstacle in sufficient time to allow the vehicle to break to a complete standstill. Unless this is done, then the vehicle will still collide with the obstacle albeit possibly at reduced speed. One approach to this problem is suggested in U.S. Pat. No. 5,429,329 and FR 2 586 391 both of which teach the use of a robotic vehicle which travels in front of a train so as to image a section of the track and relay information to the engine driver so as to provide advance warning of an obstacle on the track ahead of the engine. The use of auxiliary vehicles which are sent in advance of a railway engine, for example, allows local imaging of a section of track well in advance of the engine although it introduces other technical problems such as relaying the information back to the engine.
Another, quite different approach, is to mount the imaging camera on the engine itself, although this approach is subject to the problem of remotely imaging a section of track several kilometers ahead in order to allowing for the stopping distance of the locomotive when travelling at high speeds. It is to be noted that these two approaches, namely: (a) use of a robotically-controlled auxiliary vehicle which effects local imaging of a section of a track remote from the engine but directly in front of the auxiliary vehicle; and (b) remote imaging of a section of track which may be several kilometers from the engine; represent fundamentally different solutions to the same problem. It is clear that when a robotically-controlled auxiliary vehicle is employed, a relatively unsophisticated imaging system can be employed since the quality thereof is unlikely to be adversely affected by ambient conditions, such as weather and so on. On the other hand, when the imaging system is mounted on the track-led vehicle itself and is intended to image a section of track relatively remote therefrom, ambient conditions such as cloud, fog and so on can render the imaging system useless.
For the sake of a complete discussion of prior art, reference is also made to JP 04 266567 which relies on relaying to an engine driver a photo-reduced image of a section of track (e.g. railroad crossing). The compressed data is expanded so as to reproduce the original image which is then displayed on a monitor inside the engine so as to be visible to the driver. There is no automatic processing of the data in order to determine the presence or absence of an obstacle on the track. Rather, the required discrimination is performed manually by the driver.
It would obviously be preferable to employ a detection system which is mobile and detects any type of object on the railway track.